Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization
文献类型：会议论文
| 作者 | Shen H(申海); Zhu YL(朱云龙) ; Zhou XM(周小明); Guo HF(郭海峰); Chang CG(常春光)
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| 出版日期 | 2009 |
| 会议名称 | ACM/SIGEVO Summit on Genetic and Evolutionary Computation |
| 会议日期 | June 12–14, 2009 |
| 会议地点 | Shanghai, China |
| 关键词 | bacterial foraging numerical optimization particle swarm optimization |
| 页码 | 497-504 |
| 中文摘要 | In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual's best location and the global best location. The experimental results show that BF-PSO performs much better than BFOA for almost all test functions. That's approved that the BFOA oriented by PSO strategy improve its global optimization capability. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | ACM |
| 会议录 | Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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| 会议录出版者 | ACM |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-60558-326-6 |
| WOS记录号 | WOS:000282382900067 |
| 源URL | [http://ir.sia.cn/handle/173321/8163]  |
| 专题 | 沈阳自动化研究所_工业信息学研究室_先进制造技术研究室 |
| 推荐引用方式 GB/T 7714 | Shen H,Zhu YL,Zhou XM,et al. Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization[C]. 见:ACM/SIGEVO Summit on Genetic and Evolutionary Computation . Shanghai, China. June 12–14, 2009. |
入库方式： OAI收割
来源：沈阳自动化研究所
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